Partial least square and hierarchical clustering in ADMET modeling: prediction of blood-brain barrier permeation of α-adrenergic and imidazoline receptor ligands

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (K_d). Almost all quantitative structure-property relationship (QSPR) models that describe K_d apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (K_PE-w), PE-seawater (K_PE-sw), PVC-water (K_PVC-w) and PP-seawater (K_PP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting K_d more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with K_PE-w, K_PE-sw, K_PVC-w and K_PP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (R_adj²: 0.907-0.999), robustness (Q_BOOT²: 0.900-0.937) and predictability (R_ext²: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Implementing Silica Nanoparticles in the Study of the Airborne Transmission of SARS-CoV-2

Aerosol transmission constitutes one of the major transmission routes of the SARS-CoV-2 pathogen. Due to the pathogen's properties, research on its airborne transmission has some limitations. This paper focuses on silica nanoparticles (SiO2) of 40 and 200 nm sizes as the physicochemical markers of a single SARS-CoV-2 particle enabling experiments on the transmission of bioaerosols in public spaces. Mixtures of a determined silica concentration were sprayed on as an aerosol, whose particles, sedimented on dedicated matrices, were examined by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Since it was not possible to quantitatively identify the markers based on the obtained images, the filters exposed with the AirSampler aspirator were analyzed based on inductively coupled plasma optical emission spectroscopy (ICP-OES). The ICP-OES method enabled us to determine the concentration of silica after extracting the marker from the filter, and consequently to estimate the number of markers. The developed procedure opens up the possibility of the quantitative estimation of the spread of the coronavirus, for example in studies on the aerosol transmission of the pathogen in an open environment where biological markers-surrogates included-cannot be used.

Biological Membrane-Penetrating Peptides: Computational Prediction and Applications

Peptides comprise a versatile class of biomolecules that present a unique chemical space with diverse physicochemical and structural properties. Some classes of peptides are able to naturally cross the biological membranes, such as cell membrane and blood-brain barrier (BBB). Cell-penetrating peptides (CPPs) and blood-brain barrier-penetrating peptides (B3PPs) have been explored by the biotechnological and pharmaceutical industries to develop new therapeutic molecules and carrier systems. The computational prediction of peptides’ penetration into biological membranes has been emerged as an interesting strategy due to their high throughput and low-cost screening of large chemical libraries. Structure- and sequence-based information of peptides, as well as atomistic biophysical models, have been explored in computer-assisted discovery strategies to classify and identify new structures with pharmacokinetic properties related to the translocation through biomembranes. Computational strategies to predict the permeability into biomembranes include cheminformatic filters, molecular dynamics simulations, artificial intelligence algorithms, and statistical models, and the choice of the most adequate method depends on the purposes of the computational investigation. Here, we exhibit and discuss some principles and applications of these computational methods widely used to predict the permeability of peptides into biomembranes, exhibiting some of their pharmaceutical and biotechnological applications.

Application of in vitro PAMPA technique and in silico computational methods for blood-brain barrier permeability prediction of novel CNS drug candidates

Permeability assessment of small molecules through the blood-brain barrier (BBB) plays a significant role in the development of effective central nervous system (CNS) drug candidates. Since in vivo methods for BBB permeability estimation require a lot of time and resources, in silico and in vitro approaches are becoming increasingly popular nowadays for faster and more economical predictions in early phases of drug discovery. In this work, through application of in vitro parallel artificial membrane permeability assay (PAMPA-BBB) and in silico computational methods we aimed to examine the passive permeability of eighteen compounds, which affect serotonin and dopamine levels in the CNS. The data set was consisted of novel six human dopamine transporter (hDAT) substrates that were previously identified as the most promising lead compounds for further optimisation to achieve neuroprotective effect, twelve approved CNS drugs, and their related compounds. Firstly, PAMPA methods was used to experimentally determine effective BBB permeability (P_e) for all studied compounds and obtained results were further submitted for quantitative structure permeability relationship (QSPR) analysis. QSPR models were built by using three different statistical methods: stepwise multiple linear regression (MLR), partial least square (PLS), and support-vector machine (SVM), while their predictive capability was tested through internal and external validation. Obtained statistical parameters (MLR- R²_pred=-0.10; PLS- R²_pred=0.64, r²_m=0.69, r^/2_m=0.44; SVM- R²_pred=0.57, r²_m=0.72, r^/2_m=0.55) indicated that the SVM model is superior over others. The most important molecular descriptors (H₀p and SolvEMt_3D) were identified and used to propose structural modifications of the examined compounds in order to improve their BBB permeability. Moreover, steered molecular dynamics (SMD) simulation was employed to comprehensively investigate the permeability pathway of compounds through a lipid bilayer. Taken together, the created QSPR model could be used as a reliable and fast pre-screening tool for BBB permeability prediction of structurally related CNS compounds, while performed MD simulations provide a good foundation for future in silico examination.

Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds

Permeation through the blood–brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds. Using the fragmental (substructural) descriptors representing the occurrence number of various substructures, as well as the artificial neural network approach and the double cross-validation procedure, we have developed a predictive in silico LogBB model based on an extensive and verified dataset (529 compounds), which is applicable to diverse drugs and drug-like compounds. The model has good predictivity parameters (Q2=0.815, RMSEcv=0.318) that are similar to or better than those of the most reliable models available in the literature. Larger datasets, and perhaps more sophisticated network architectures, are required to realize the full potential of deep neural networks. The analysis of fragment contributions reveals patterns of influence consistent with the known concepts of structural characteristics that affect the BBB permeability of organic compounds. The external validation of the model confirms good agreement between the predicted and experimental LogBB values for most of the compounds. The model enables the evaluation and optimization of the BBB permeability of potential neuroactive agents and other drug compounds.

The impact of tamsulosin on cognition in Alzheimer disease with benign prostate hyperplasia: A study using the Hallym Smart Clinical Data Warehouse

Studies suggest that the use of alpha-blockers increases the risk of dementia in patients with benign prostate hyperplasia (BPH). Due to study limitations, the relationship between the use of alpha-blockers, such as tamsulosin, and the risk of dementia is still unclear. However, alpha1-adrenoreceptors are also present in the brain, so there is potential for adverse effects on cognitive function. Therefore, we investigated possible associations between the use of alpha-blockers and aggravation of cognitive decline in dementia patients using a clinical data analytic solution called the Smart Clinical Data Warehouse (CDW).We retrospectively investigated clinical data using the Smart CDW of Hallym University Medical Center from 2009 to 2019. We enrolled patients with probable Alzheimer disease (AD) who had completed the Mini-Mental State Examination (MMSE) at least twice during follow-up, and who had BPH. We compared the difference in MMSE scores between patients who took tamsulosin for >1000 days and those who did not take any alpha-blocker. We tested the effect of tamsulosin on cognitive decline in patients with AD, using propensity score-matched logistic regression analysis.Eligible cases were included in the tamsulosin (n = 68) or no-medication (n = 153) groups. After propensity score matching, clinical characteristics such as educational attainment and vascular risk factors were similar in the tamsulosin and no-medication groups. The MMSE scores did not differ significantly between the tamsulosin and no-medication groups (P = .470).The results suggest that tamsulosin for BPH is not associated with worsening of the cognitive decline in patients with AD.

Profile of CO2, CO, and H2 Emissions from Thermal Oxidation of Polish Coals

The self-heating phenomenon of coal leads to work safety hazards in underground mining. The quantitative analysis of gaseous products in mine atmosphere constitutes one of the detection methods of advanced coal heating. The article presents the results of tests on CO, CO₂, and H₂ emissions during simulated heating of coal in the temperature range of 323–523 K. The oxidation of 15 Polish coals of various carbon contents was performed using a flow reactor technique. A chromatography method was applied to measure the changes of oxidation products concentrations with the increase of temperature. It has been determined that all the tested gases were generated at the initial temperature. The collected data indicated that CO₂ was a major oxidation product in the entire temperature range, while the amounts of H₂ produced did not exceed 0.49% volume. At the temperature of 323 K, the ratio of CO₂/CO was in the range of 10–23 but along with the temperature increase the ratio range narrowed to 3–4. In this paper, a comparison of the physical-chemical properties of the tested coals and the emissions profile of the gases using, among others, the hierarchical clustering analysis showed that samples with higher oxygen, sulfur, and inertinite content as well as lower ash and carbon content formed larger amounts of fire gases.

Cognitive and mood side effects of lower urinary tract medication

Introduction: Muscarinic receptor antagonists, 5α-reductase inhibitors and α₁-adrenoceptor antagonists are frequently used drug classes for the treatment of lower urinary tract symptoms including those of overactive bladder syndrome and benign prostatic enlargement/benign prostatic obstruction. Areas covered: The authors review the evidence for adverse effects of these drug classes on cognitive function, mood and other functions of the central nervous system and discuss such effects against the evidence for mechanistic plausibility. Expert opinion: Muscarinic antagonists carry a risk for impaired cognition and other brain functions that differs quantitatively between compounds, being highest with oral formulations of oxybutynin. 5□-Reductase inhibitors can cause depressive symptoms even at low doses and starting several months after discontinuation of treatment. The evidence for α₁-adrenoceptor antagonists and specifically tamsulosin to cause dementia is controversial and lacks mechanistic plausibility. We recommend that physicians treating patients with lower urinary tract symptoms carefully monitor mental status prior to prescribing and periodically thereafter.

Novel tetrahydroacridine derivatives with iodobenzoic moieties induce G0/G1 cell cycle arrest and apoptosis in A549 non-small lung cancer and HT-29 colorectal cancer cells

A series of nine tetrahydroacridine derivatives with iodobenzoic moiety were synthesized and evaluated for their cytotoxic activity against cancer cell lines—A549 (human lung adenocarcinoma), HT-29 (human colorectal adenocarcinoma) and somatic cell line—EA.hy926 (human umbilical vein cell line). All compounds displayed high cytotoxicity activity against A549 (IC₅₀ 59.12–14.87 µM) and HT-29 (IC₅₀ 17.32–5.90 µM) cell lines, higher than control agents—etoposide and 5-fluorouracil. Structure–activity relationship showed that the position of iodine in the substituent in the para position and longer linker most strongly enhanced the cytotoxic effect. Among derivatives, 1i turned out to be the most cytotoxic and displayed IC₅₀ values of 14.87 µM against A549 and 5.90 µM against HT-29 cell lines. In hyaluronidase inhibition assay, all compounds presented anti-inflammatory activity, however, slightly lower than reference compound. ADMET prediction showed that almost all compounds had good pharmacokinetic profiles. 1b, 1c and 1f compounds turned out to act against chemoresistance in cisplatin-resistant 253J B-V cells. Compounds intercalated into DNA and inhibited cell cycle in G0/G1 phase—the strongest inhibition was observed for 1i in A549 and 1c in HT-29. Among compounds, the highest apoptotic effect in both cell lines was observed after treatment with 1i. Compounds caused DNA damage and H2AX phosphorylation, which was detected in A549 and HT-29 cells. All research confirmed anticancer properties of novel tetrahydroacridine derivatives and explained a few pathways of their mechanism of cytotoxic action.

Study of the Hazard of Endogenous Fires in Coal Mines—A Chemometric Approach

The most commonly used practice to assess fire hazard development in underground coal mines is based on the measurement of the concentration of selected gases in the mine’s air. The main goal of this study was present a strategy to monitor the gaseous atmosphere in the mine in order to identify the onset of an endogenous fire in the coal seam. For that purpose, the principal component analysis (PCA) and the hierarchical clustering analysis (HCA) were applied. The monitoring covers the measurements of concentration of CO, CO2, H2, O2, N2, and selected hydrocarbons, respectively throughout the whole of one year. The chemometric methods applied allow for effective exploration of the similarities between the studied samples collected both under fire hazard conditions and under safe conditions. Based on the constructed models, the groups of objects characterized with the highest content of ethylene, acetylene, propylene, and carbon monoxide were identified. These samples indicate the endogenic fire in coal mine.

ADME properties evaluation in drug discovery: in silico prediction of blood-brain partitioning

The absorption, distribution, metabolism and excretion properties are important for drugs, and prediction of these properties in advance will save the cost of drug discovery substantially. The ability to penetrate the blood-brain barrier is critical for drugs targeting central nervous system, which is represented by the ratio of its concentration in brain and in blood. Herein, a quantitative structure-property relationship study was carried out to predict blood-brain partitioning coefficient (logBB) of a data set consisting of 287 compounds. Four different methods including support vector machine, multivariate linear regression, multivariate adaptive regression splines and random forest were employed to build prediction models with 116 molecular descriptors selected by Boruta algorithm. The RF model had best performance in training set ([Formula: see text] = 0.938), test set ([Formula: see text] = 0.840) and tenfold cross-validation ([Formula: see text] = 0.788). Finally, we found that the polar surface area and octanol-water partition coefficient have the greatest influence on blood-brain partitioning. Results suggest that the proposed model is a useful and practical tool to predict the logBB values of drug candidates.

Is Tamsulosin Linked to Dementia in the Elderly?

PURPOSE OF REVIEW

Lower urinary tract symptoms (LUTS) result from age-related changes in detrusor function and prostatic growth that are driven by alterations in the ratio of circulating androgens and estrogens. Alpha-adrenergic receptor blockers are commonly used to treat LUTS because they influence urethral tone and intra-urethral pressure. Molecular cloning studies have identified three α₁-adrenergic receptor subtypes (α_1A, α_1B, and α_1D). The α_1A subtype is predominant in the human prostate but is also present in many parts of the brain that direct cognitive function. Tamsulosin is the most widely used α₁-adrenergic receptor antagonist with 12.6 million prescriptions filled in 2010 alone. When compared to the other common types of α₁-adrenergic receptor antagonists (i.e., terazosin, doxazosin, and alfuzosin), tamsulosin is 10- to 38-fold more selective for the α_1A versus the α_1B subtype.

RECENT FINDINGS

Duan et al. have recently shown that men taking tamsulosin have a higher risk of developing dementia when compared to men taking other α-adrenergic antagonists or no α-adrenergic antagonists at all (HR 1.17; 95% CI 1.14-1.21). Based upon this retrospective analysis, we believe that tamsulosin, because of its unique affinity for α_1A-adrenergic receptors, may increase the risk of developing dementia when used for an extended period of time. If these findings are confirmed, they carry significant public health implications for an aging society.

A Comparative Study of Chromatographic Behavior and Lipophilicity of Selected Imidazoline Derivatives

Chromatographic behavior and lipophilicity of 20 selected imidazoline derivatives were examined by thin-layer chromatography using CN, RP-2, RP-8 and RP-18 as the stationary phases and a mixture of methanol, water and ammonia as the mobile phase. In all examined chromatographic systems, linear relationships were established between retention parameters and the volume fraction of methanol in the mobile phase (r > 0.985, 0.978, 0.981, 0.988 for the CN, RP-2, RP-8 and RP-18, respectively). The highest correlation between the obtained [Formula: see text] values was observed for RP-2 and RP-8 stationary phases. The experimental lipophilicity indices ([Formula: see text], m and C0) obtained from the retention data were used in correlation study with the calculated logP values. Experimentally determined [Formula: see text] values for all investigated chromatographic systems exhibited the highest correlation with the calculated ClogP values (r: 0.880, 0.872, 0.897 and 0.889 for the CN, RP-2, RP-8 and RP-18 stationary phases, respectively). In addition, principal component analysis enables new information about similarity and differences between tested compounds as well as experimental lipophilicity indices and calculated logP values. Performed QSRR analysis showed that the frequency of C-C at topological distance 1 and CATS2D Lipophilic-Lipophilic at lag 01 were important descriptors with influence on the [Formula: see text] values in all the examined chromatographic systems, while the differences in the retention behavior of compounds on the examined stationary phases can be distinguished based on their specific geometrical, electronic and constitutional properties.

Phosphatidylinositol 3-Kinase/AKT Pathway Inhibition by Doxazosin Promotes Glioblastoma Cells Death, Upregulation of p53 and Triggers Low Neurotoxicity

Glioblastoma is the most frequent and malignant brain tumor. Treatment includes chemotherapy with temozolomide concomitant with surgical resection and/or irradiation. However, a number of cases are resistant to temozolomide, as well as the human glioblastoma cell line U138-MG. We investigated doxazosin’s (an antihypertensive drug) activity against glioblastoma cells (C6 and U138-MG) and its neurotoxicity on primary astrocytes and organoptypic hippocampal cultures. For this study, the following methods were used: citotoxicity assays, flow cytometry, western-blotting and confocal microscopy. We showed that doxazosin induces cell death on C6 and U138-MG cells. We observed that doxazosin’s effects on the PI3K/Akt pathway were similar as LY294002 (PI3K specific inhibitor). In glioblastoma cells treated with doxasozin, Akt levels were greatly reduced. Upon examination of activities of proteins downstream of Akt we observed upregulation of GSK-3β and p53. This led to cell proliferation inhibition, cell death induction via caspase-3 activation and cell cycle arrest at G0/G1 phase in glioblastoma cells. We used in this study Lapatinib, a tyrosine kinase inhibitor, as a comparison with doxazosin because they present similar chemical structure. We also tested the neurocitotoxicity of doxazosin in primary astrocytes and organotypic cultures and observed that doxazosin induced cell death on a small percentage of non-tumor cells. Aggressiveness of glioblastoma tumors and dismal prognosis require development of new treatment agents. This includes less toxic drugs, more selective towards tumor cells, causing less damage to the patient. Therefore, our results confirm the potential of doxazosin as an attractive therapeutic antiglioma agent.

Procognitive properties of drugs with single and multitargeting H3 receptor antagonist activities

The histamine H3 receptor (H3 R) is an important modulator of numerous central control mechanisms. Novel lead optimizations for H3 R antagonists/inverse agonists involved studies of structure-activity relationships, cross-affinities, and pharmacokinetic properties of promising ligands. Blockade of inhibitory histamine H3 autoreceptors reinforces histaminergic transmission, while antagonism of H3 heteroreceptors accelerates the corticolimbic liberation of acetylcholine, norepinephrine, glutamate, dopamine, serotonin and gamma-aminobutyric acid (GABA). The H3 R positioned at numerous neurotransmission crossroads indicates therapeutic applications of small-molecule H3 R modulators in a number of psychiatric and neurodegenerative diseases with various clinical candidates available. Dual target drugs displaying H3 R antagonism/inverse agonism with inhibition of acetylcholine esterase (AChE), histamine N-methyltransferase (HMT), or serotonin transporter (SERT) are novel class of procognitive agents. Main chemical diversities, pharmacophores, and pharmacological profiles of procognitive agents acting as H3 R antagonists/inverse agonists and dual H3 R antagonists/inverse agonists with inhibiting activity on AChE, HMT, or SERT are highlighted here.